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.col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { position: relative; min-height: 1px; padding-left: 0px; padding-right: 0px;}.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { float: left;}.col-xs-12 { width: 100%;}.col-xs-11 { width: 91.66666667%;}.col-xs-10 { width: 83.33333333%;}.col-xs-9 { width: 75%;}.col-xs-8 { width: 66.66666667%;}.col-xs-7 { width: 58.33333333%;}.col-xs-6 { width: 50%;}.col-xs-5 { width: 41.66666667%;}.col-xs-4 { width: 33.33333333%;}.col-xs-3 { width: 25%;}.col-xs-2 { width: 16.66666667%;}.col-xs-1 { width: 8.33333333%;}.col-xs-pull-12 { right: 100%;}.col-xs-pull-11 { right: 91.66666667%;}.col-xs-pull-10 { right: 83.33333333%;}.col-xs-pull-9 { right: 75%;}.col-xs-pull-8 { right: 66.66666667%;}.col-xs-pull-7 { right: 58.33333333%;}.col-xs-pull-6 { right: 50%;}.col-xs-pull-5 { right: 41.66666667%;}.col-xs-pull-4 { right: 33.33333333%;}.col-xs-pull-3 { right: 25%;}.col-xs-pull-2 { right: 16.66666667%;}.col-xs-pull-1 { right: 8.33333333%;}.col-xs-pull-0 { right: auto;}.col-xs-push-12 { left: 100%;}.col-xs-push-11 { left: 91.66666667%;}.col-xs-push-10 { left: 83.33333333%;}.col-xs-push-9 { left: 75%;}.col-xs-push-8 { left: 66.66666667%;}.col-xs-push-7 { left: 58.33333333%;}.col-xs-push-6 { left: 50%;}.col-xs-push-5 { left: 41.66666667%;}.col-xs-push-4 { left: 33.33333333%;}.col-xs-push-3 { left: 25%;}.col-xs-push-2 { left: 16.66666667%;}.col-xs-push-1 { left: 8.33333333%;}.col-xs-push-0 { left: auto;}.col-xs-offset-12 { margin-left: 100%;}.col-xs-offset-11 { margin-left: 91.66666667%;}.col-xs-offset-10 { margin-left: 83.33333333%;}.col-xs-offset-9 { margin-left: 75%;}.col-xs-offset-8 { margin-left: 66.66666667%;}.col-xs-offset-7 { margin-left: 58.33333333%;}.col-xs-offset-6 { margin-left: 50%;}.col-xs-offset-5 { margin-left: 41.66666667%;}.col-xs-offset-4 { margin-left: 33.33333333%;}.col-xs-offset-3 { margin-left: 25%;}.col-xs-offset-2 { margin-left: 16.66666667%;}.col-xs-offset-1 { margin-left: 8.33333333%;}.col-xs-offset-0 { margin-left: 0%;}@media (min-width: 768px) { .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { float: left; } .col-sm-12 { width: 100%; } .col-sm-11 { width: 91.66666667%; } .col-sm-10 { width: 83.33333333%; } .col-sm-9 { width: 75%; } .col-sm-8 { width: 66.66666667%; } .col-sm-7 { width: 58.33333333%; } .col-sm-6 { width: 50%; } .col-sm-5 { width: 41.66666667%; } .col-sm-4 { width: 33.33333333%; } .col-sm-3 { width: 25%; } .col-sm-2 { width: 16.66666667%; } .col-sm-1 { width: 8.33333333%; } .col-sm-pull-12 { right: 100%; } .col-sm-pull-11 { right: 91.66666667%; } .col-sm-pull-10 { right: 83.33333333%; } .col-sm-pull-9 { right: 75%; } .col-sm-pull-8 { right: 66.66666667%; } .col-sm-pull-7 { right: 58.33333333%; } .col-sm-pull-6 { right: 50%; } .col-sm-pull-5 { right: 41.66666667%; } .col-sm-pull-4 { right: 33.33333333%; } .col-sm-pull-3 { right: 25%; } .col-sm-pull-2 { right: 16.66666667%; } .col-sm-pull-1 { right: 8.33333333%; } .col-sm-pull-0 { right: auto; } .col-sm-push-12 { left: 100%; } .col-sm-push-11 { left: 91.66666667%; } .col-sm-push-10 { left: 83.33333333%; } .col-sm-push-9 { left: 75%; } .col-sm-push-8 { left: 66.66666667%; } .col-sm-push-7 { left: 58.33333333%; } .col-sm-push-6 { left: 50%; } .col-sm-push-5 { left: 41.66666667%; } .col-sm-push-4 { left: 33.33333333%; } .col-sm-push-3 { left: 25%; } .col-sm-push-2 { left: 16.66666667%; } .col-sm-push-1 { left: 8.33333333%; } .col-sm-push-0 { left: auto; } .col-sm-offset-12 { margin-left: 100%; } .col-sm-offset-11 { margin-left: 91.66666667%; } .col-sm-offset-10 { margin-left: 83.33333333%; } .col-sm-offset-9 { margin-left: 75%; } .col-sm-offset-8 { margin-left: 66.66666667%; } .col-sm-offset-7 { margin-left: 58.33333333%; } .col-sm-offset-6 { margin-left: 50%; } .col-sm-offset-5 { margin-left: 41.66666667%; } .col-sm-offset-4 { margin-left: 33.33333333%; } .col-sm-offset-3 { margin-left: 25%; } .col-sm-offset-2 { margin-left: 16.66666667%; } .col-sm-offset-1 { margin-left: 8.33333333%; } .col-sm-offset-0 { margin-left: 0%; }}@media (min-width: 992px) { .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { float: left; } .col-md-12 { width: 100%; } .col-md-11 { width: 91.66666667%; } .col-md-10 { width: 83.33333333%; } .col-md-9 { width: 75%; } .col-md-8 { width: 66.66666667%; } .col-md-7 { width: 58.33333333%; } .col-md-6 { width: 50%; } .col-md-5 { width: 41.66666667%; } .col-md-4 { width: 33.33333333%; } .col-md-3 { width: 25%; } .col-md-2 { width: 16.66666667%; } .col-md-1 { width: 8.33333333%; } .col-md-pull-12 { right: 100%; } .col-md-pull-11 { right: 91.66666667%; } .col-md-pull-10 { right: 83.33333333%; } .col-md-pull-9 { right: 75%; } .col-md-pull-8 { right: 66.66666667%; } .col-md-pull-7 { right: 58.33333333%; } .col-md-pull-6 { right: 50%; } .col-md-pull-5 { right: 41.66666667%; } .col-md-pull-4 { right: 33.33333333%; } .col-md-pull-3 { right: 25%; } .col-md-pull-2 { right: 16.66666667%; } .col-md-pull-1 { right: 8.33333333%; } .col-md-pull-0 { right: auto; } .col-md-push-12 { left: 100%; } .col-md-push-11 { left: 91.66666667%; } .col-md-push-10 { left: 83.33333333%; } .col-md-push-9 { left: 75%; } .col-md-push-8 { left: 66.66666667%; } .col-md-push-7 { left: 58.33333333%; } .col-md-push-6 { left: 50%; } .col-md-push-5 { left: 41.66666667%; } .col-md-push-4 { left: 33.33333333%; } .col-md-push-3 { left: 25%; } .col-md-push-2 { left: 16.66666667%; } .col-md-push-1 { left: 8.33333333%; } .col-md-push-0 { left: auto; } .col-md-offset-12 { margin-left: 100%; } .col-md-offset-11 { margin-left: 91.66666667%; } .col-md-offset-10 { margin-left: 83.33333333%; } .col-md-offset-9 { margin-left: 75%; } .col-md-offset-8 { margin-left: 66.66666667%; } .col-md-offset-7 { margin-left: 58.33333333%; } .col-md-offset-6 { margin-left: 50%; } .col-md-offset-5 { margin-left: 41.66666667%; } .col-md-offset-4 { margin-left: 33.33333333%; } .col-md-offset-3 { margin-left: 25%; } .col-md-offset-2 { margin-left: 16.66666667%; } .col-md-offset-1 { margin-left: 8.33333333%; } .col-md-offset-0 { margin-left: 0%; }}@media (min-width: 1200px) { .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { float: left; } .col-lg-12 { width: 100%; } .col-lg-11 { width: 91.66666667%; } .col-lg-10 { width: 83.33333333%; } .col-lg-9 { width: 75%; } .col-lg-8 { width: 66.66666667%; } .col-lg-7 { width: 58.33333333%; } .col-lg-6 { width: 50%; } .col-lg-5 { width: 41.66666667%; } .col-lg-4 { width: 33.33333333%; } .col-lg-3 { width: 25%; } .col-lg-2 { width: 16.66666667%; } .col-lg-1 { width: 8.33333333%; } .col-lg-pull-12 { right: 100%; } .col-lg-pull-11 { right: 91.66666667%; } .col-lg-pull-10 { right: 83.33333333%; } .col-lg-pull-9 { right: 75%; } .col-lg-pull-8 { right: 66.66666667%; } .col-lg-pull-7 { right: 58.33333333%; } .col-lg-pull-6 { right: 50%; } .col-lg-pull-5 { right: 41.66666667%; } .col-lg-pull-4 { right: 33.33333333%; } .col-lg-pull-3 { right: 25%; } .col-lg-pull-2 { right: 16.66666667%; } .col-lg-pull-1 { right: 8.33333333%; } .col-lg-pull-0 { right: auto; } .col-lg-push-12 { left: 100%; } .col-lg-push-11 { left: 91.66666667%; } .col-lg-push-10 { left: 83.33333333%; } .col-lg-push-9 { left: 75%; } .col-lg-push-8 { left: 66.66666667%; } .col-lg-push-7 { left: 58.33333333%; } .col-lg-push-6 { left: 50%; } .col-lg-push-5 { left: 41.66666667%; } .col-lg-push-4 { left: 33.33333333%; } .col-lg-push-3 { left: 25%; } .col-lg-push-2 { left: 16.66666667%; } .col-lg-push-1 { left: 8.33333333%; } .col-lg-push-0 { left: auto; } .col-lg-offset-12 { margin-left: 100%; } .col-lg-offset-11 { margin-left: 91.66666667%; } .col-lg-offset-10 { margin-left: 83.33333333%; } .col-lg-offset-9 { margin-left: 75%; } .col-lg-offset-8 { margin-left: 66.66666667%; } .col-lg-offset-7 { margin-left: 58.33333333%; } .col-lg-offset-6 { margin-left: 50%; } .col-lg-offset-5 { margin-left: 41.66666667%; } .col-lg-offset-4 { margin-left: 33.33333333%; } .col-lg-offset-3 { margin-left: 25%; } .col-lg-offset-2 { margin-left: 16.66666667%; } .col-lg-offset-1 { margin-left: 8.33333333%; } .col-lg-offset-0 { margin-left: 0%; }}table { background-color: transparent;}caption { padding-top: 8px; padding-bottom: 8px; color: #777777; text-align: left;}th { text-align: left;}.table { width: 100%; max-width: 100%; margin-bottom: 18px;}.table > thead > tr > th,.table > tbody > tr > th,.table > tfoot > tr > th,.table > thead > tr > td,.table > tbody > tr > td,.table > tfoot > tr > td { padding: 8px; line-height: 1.42857143; vertical-align: top; border-top: 1px solid #ddd;}.table > thead > tr > th { vertical-align: bottom; border-bottom: 2px solid #ddd;}.table > caption + thead > tr:first-child > th,.table > colgroup + thead > tr:first-child > th,.table > thead:first-child > tr:first-child > th,.table > caption + thead > tr:first-child > td,.table > colgroup + thead > tr:first-child > td,.table > thead:first-child > tr:first-child > td { border-top: 0;}.table > tbody + tbody { border-top: 2px solid #ddd;}.table .table { background-color: #fff;}.table-condensed > thead > tr > th,.table-condensed > tbody > tr > th,.table-condensed > tfoot > tr > th,.table-condensed > thead > tr > td,.table-condensed > tbody > tr > td,.table-condensed > tfoot > tr > td { padding: 5px;}.table-bordered { border: 1px solid 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tr.warning > th { background-color: #fcf8e3;}.table-hover > tbody > tr > td.warning:hover,.table-hover > tbody > tr > th.warning:hover,.table-hover > tbody > tr.warning:hover > td,.table-hover > tbody > tr:hover > .warning,.table-hover > tbody > tr.warning:hover > th { background-color: #faf2cc;}.table > thead > tr > td.danger,.table > tbody > tr > td.danger,.table > tfoot > tr > td.danger,.table > thead > tr > th.danger,.table > tbody > tr > th.danger,.table > tfoot > tr > th.danger,.table > thead > tr.danger > td,.table > tbody > tr.danger > td,.table > tfoot > tr.danger > td,.table > thead > tr.danger > th,.table > tbody > tr.danger > th,.table > tfoot > tr.danger > th { background-color: #f2dede;}.table-hover > tbody > tr > td.danger:hover,.table-hover > tbody > tr > th.danger:hover,.table-hover > tbody > tr.danger:hover > td,.table-hover > tbody > tr:hover > .danger,.table-hover > tbody > tr.danger:hover > th { background-color: #ebcccc;}.table-responsive { overflow-x: auto; min-height: 0.01%;}@media screen and (max-width: 767px) { .table-responsive { width: 100%; margin-bottom: 13.5px; overflow-y: hidden; overflow-style: autohiding-scrollbar; border: 1px solid #ddd; } .table-responsive > .table { margin-bottom: 0; } .table-responsive > .table > thead > tr > th, .table-responsive > .table > tbody > tr > th, .table-responsive > .table > tfoot > tr > th, .table-responsive > .table > thead > tr > td, .table-responsive > .table > tbody > tr > td, .table-responsive > .table > tfoot > tr > td { white-space: nowrap; } .table-responsive > .table-bordered { border: 0; } .table-responsive > .table-bordered > thead > tr > th:first-child, .table-responsive > .table-bordered > tbody > tr > th:first-child, .table-responsive > .table-bordered > tfoot > tr > th:first-child, .table-responsive > .table-bordered > thead > tr > td:first-child, .table-responsive > .table-bordered > tbody > tr > td:first-child, .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .table-responsive > .table-bordered > thead > tr > th:last-child, .table-responsive > .table-bordered > tbody > tr > th:last-child, .table-responsive > .table-bordered > tfoot > tr > th:last-child, .table-responsive > .table-bordered > thead > tr > td:last-child, .table-responsive > .table-bordered > tbody > tr > td:last-child, .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .table-responsive > .table-bordered > tbody > tr:last-child > th, .table-responsive > .table-bordered > tfoot > tr:last-child > th, .table-responsive > .table-bordered > tbody > tr:last-child > td, .table-responsive > .table-bordered > tfoot > tr:last-child > td { border-bottom: 0; }}fieldset { padding: 0; margin: 0; border: 0; min-width: 0;}legend { display: block; width: 100%; padding: 0; margin-bottom: 18px; font-size: 19.5px; line-height: inherit; color: #333333; border: 0; border-bottom: 1px solid #e5e5e5;}label { display: inline-block; max-width: 100%; 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Elsewhere // we use CSS to left justify single line equations in code cells. displayAlign: 'center', "HTML-CSS": { styles: {'.MathJax_Display': {"margin": 0}}, linebreaks: { automatic: true } } }); </script> <!-- End of mathjax configuration --></head><body> <div tabindex="-1" id="notebook" class="border-box-sizing"> <div class="container" id="notebook-container"><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><h1 id="Your-first-neural-network">Your first neural network<a class="anchor-link" href="#Your-first-neural-network">¶</a></h1><p>In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.</p></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [1]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = 'retina'<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span></pre></div></div></div></div></div><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><h2 id="Load-and-prepare-the-data">Load and prepare the data<a class="anchor-link" href="#Load-and-prepare-the-data">¶</a></h2><p>A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!</p></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [2]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_path</span> <span class="o">=</span> <span class="s1">'Bike-Sharing-Dataset/hour.csv'</span><span class="n">rides</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">)</span></pre></div></div></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [3]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="n">rides</span><span class="o">.</span><span class="n">head</span><span class="p">()</span></pre></div></div></div></div><div class="output_wrapper"><div class="output"><div class="output_area"><div class="prompt output_prompt">Out[3]:</div><div class="output_html rendered_html output_subarea output_execute_result"><div><table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>instant</th> <th>dteday</th> <th>season</th> <th>yr</th> <th>mnth</th> <th>hr</th> <th>holiday</th> <th>weekday</th> <th>workingday</th> <th>weathersit</th> <th>temp</th> <th>atemp</th> <th>hum</th> <th>windspeed</th> <th>casual</th> <th>registered</th> <th>cnt</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> <td>2011-01-01</td> <td>1</td> <td>0</td> <td>1</td> <td>0</td> <td>0</td> <td>6</td> <td>0</td> <td>1</td> <td>0.24</td> <td>0.2879</td> <td>0.81</td> <td>0.0</td> <td>3</td> <td>13</td> <td>16</td> </tr> <tr> <th>1</th> <td>2</td> <td>2011-01-01</td> <td>1</td> <td>0</td> <td>1</td> <td>1</td> <td>0</td> <td>6</td> <td>0</td> <td>1</td> <td>0.22</td> <td>0.2727</td> <td>0.80</td> <td>0.0</td> <td>8</td> <td>32</td> <td>40</td> </tr> <tr> <th>2</th> <td>3</td> <td>2011-01-01</td> <td>1</td> <td>0</td> <td>1</td> <td>2</td> <td>0</td> <td>6</td> <td>0</td> <td>1</td> <td>0.22</td> <td>0.2727</td> <td>0.80</td> <td>0.0</td> <td>5</td> <td>27</td> <td>32</td> </tr> <tr> <th>3</th> <td>4</td> <td>2011-01-01</td> <td>1</td> <td>0</td> <td>1</td> <td>3</td> <td>0</td> <td>6</td> <td>0</td> <td>1</td> <td>0.24</td> <td>0.2879</td> <td>0.75</td> <td>0.0</td> <td>3</td> <td>10</td> <td>13</td> </tr> <tr> <th>4</th> <td>5</td> <td>2011-01-01</td> <td>1</td> <td>0</td> <td>1</td> <td>4</td> <td>0</td> <td>6</td> <td>0</td> <td>1</td> <td>0.24</td> <td>0.2879</td> <td>0.75</td> <td>0.0</td> <td>0</td> <td>1</td> <td>1</td> </tr> </tbody></table></div></div></div></div></div></div><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><h2 id="Checking-out-the-data">Checking out the data<a class="anchor-link" href="#Checking-out-the-data">¶</a></h2><p>This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the <code>cnt</code> column. You can see the first few rows of the data above.</p><p>Below is a plot showing the number of bike riders over the first 10 days in the data set. You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.</p></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [4]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="n">rides</span><span class="p">[:</span><span class="mi">24</span><span class="o">*</span><span class="mi">10</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">'dteday'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'cnt'</span><span class="p">)</span></pre></div></div></div></div><div class="output_wrapper"><div class="output"><div class="output_area"><div class="prompt output_prompt">Out[4]:</div><div class="output_text output_subarea output_execute_result"><pre><matplotlib.axes._subplots.AxesSubplot at 0x273e715c7f0></pre></div></div><div class="output_area"><div class="prompt"></div><div class="output_png output_subarea "><img 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"width=380height=263></div></div></div></div></div><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><h3 id="Dummy-variables">Dummy variables<a class="anchor-link" href="#Dummy-variables">¶</a></h3><p>Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to <code>get_dummies()</code>.</p></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [5]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="n">dummy_fields</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'season'</span><span class="p">,</span> <span class="s1">'weathersit'</span><span class="p">,</span> <span class="s1">'mnth'</span><span class="p">,</span> <span class="s1">'hr'</span><span class="p">,</span> <span class="s1">'weekday'</span><span class="p">]</span><span class="k">for</span> <span class="n">each</span> <span class="ow">in</span> <span class="n">dummy_fields</span><span class="p">:</span> <span class="n">dummies</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">rides</span><span class="p">[</span><span class="n">each</span><span class="p">],</span> <span class="n">prefix</span><span class="o">=</span><span class="n">each</span><span class="p">,</span> <span class="n">drop_first</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="n">rides</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">rides</span><span class="p">,</span> <span class="n">dummies</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="n">fields_to_drop</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'instant'</span><span class="p">,</span> <span class="s1">'dteday'</span><span class="p">,</span> <span class="s1">'season'</span><span class="p">,</span> <span class="s1">'weathersit'</span><span class="p">,</span> <span class="s1">'weekday'</span><span class="p">,</span> <span class="s1">'atemp'</span><span class="p">,</span> <span class="s1">'mnth'</span><span class="p">,</span> <span class="s1">'workingday'</span><span class="p">,</span> <span class="s1">'hr'</span><span class="p">]</span><span class="n">data</span> <span class="o">=</span> <span class="n">rides</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">fields_to_drop</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span></pre></div></div></div></div><div class="output_wrapper"><div class="output"><div class="output_area"><div class="prompt output_prompt">Out[5]:</div><div class="output_html rendered_html output_subarea output_execute_result"><div><table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>yr</th> <th>holiday</th> <th>temp</th> <th>hum</th> <th>windspeed</th> <th>casual</th> <th>registered</th> <th>cnt</th> <th>season_1</th> <th>season_2</th> <th>...</th> <th>hr_21</th> <th>hr_22</th> <th>hr_23</th> <th>weekday_0</th> <th>weekday_1</th> <th>weekday_2</th> <th>weekday_3</th> <th>weekday_4</th> <th>weekday_5</th> <th>weekday_6</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>0</td> <td>0</td> <td>0.24</td> <td>0.81</td> <td>0.0</td> <td>3</td> <td>13</td> <td>16</td> <td>1</td> <td>0</td> <td>...</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>1</th> <td>0</td> <td>0</td> <td>0.22</td> <td>0.80</td> <td>0.0</td> <td>8</td> <td>32</td> <td>40</td> <td>1</td> <td>0</td> <td>...</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>2</th> <td>0</td> <td>0</td> <td>0.22</td> <td>0.80</td> <td>0.0</td> <td>5</td> <td>27</td> <td>32</td> <td>1</td> <td>0</td> <td>...</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>3</th> <td>0</td> <td>0</td> <td>0.24</td> <td>0.75</td> <td>0.0</td> <td>3</td> <td>10</td> <td>13</td> <td>1</td> <td>0</td> <td>...</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <th>4</th> <td>0</td> <td>0</td> <td>0.24</td> <td>0.75</td> <td>0.0</td> <td>0</td> <td>1</td> <td>1</td> <td>1</td> <td>0</td> <td>...</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> </tr> </tbody></table><p>5 rows à 59 columns</p></div></div></div></div></div></div><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><h3 id="Scaling-target-variables">Scaling target variables<a class="anchor-link" href="#Scaling-target-variables">¶</a></h3><p>To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.</p><p>The scaling factors are saved so we can go backwards when we use the network for predictions.</p></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [6]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="n">quant_features</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'casual'</span><span class="p">,</span> <span class="s1">'registered'</span><span class="p">,</span> <span class="s1">'cnt'</span><span class="p">,</span> <span class="s1">'temp'</span><span class="p">,</span> <span class="s1">'hum'</span><span class="p">,</span> <span class="s1">'windspeed'</span><span class="p">]</span><span class="c1"># Store scalings in a dictionary so we can convert back later</span><span class="n">scaled_features</span> <span class="o">=</span> <span class="p">{}</span><span class="k">for</span> <span class="n">each</span> <span class="ow">in</span> <span class="n">quant_features</span><span class="p">:</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">each</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">data</span><span class="p">[</span><span class="n">each</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span> <span class="n">scaled_features</span><span class="p">[</span><span class="n">each</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">]</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">each</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">each</span><span class="p">]</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span><span class="o">/</span><span class="n">std</span></pre></div></div></div></div></div><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><h3 id="Splitting-the-data-into-training,-testing,-and-validation-sets">Splitting the data into training, testing, and validation sets<a class="anchor-link" href="#Splitting-the-data-into-training,-testing,-and-validation-sets">¶</a></h3><p>We'll save the last 21 days of the data to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.</p></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [7]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Save the last 21 days </span><span class="n">test_data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="mi">21</span><span class="o">*</span><span class="mi">24</span><span class="p">:]</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="o">-</span><span class="mi">21</span><span class="o">*</span><span class="mi">24</span><span class="p">]</span><span class="c1"># Separate the data into features and targets</span><span class="n">target_fields</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'cnt'</span><span class="p">,</span> <span class="s1">'casual'</span><span class="p">,</span> <span class="s1">'registered'</span><span class="p">]</span><span class="n">features</span><span class="p">,</span> <span class="n">targets</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">target_fields</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">data</span><span class="p">[</span><span class="n">target_fields</span><span class="p">]</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_targets</span> <span class="o">=</span> <span class="n">test_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">target_fields</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">test_data</span><span class="p">[</span><span class="n">target_fields</span><span class="p">]</span></pre></div></div></div></div></div><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><p>We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).</p></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [8]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Hold out the last 60 days of the remaining data as a validation set</span><span class="n">train_features</span><span class="p">,</span> <span class="n">train_targets</span> <span class="o">=</span> <span class="n">features</span><span class="p">[:</span><span class="o">-</span><span class="mi">60</span><span class="o">*</span><span class="mi">24</span><span class="p">],</span> <span class="n">targets</span><span class="p">[:</span><span class="o">-</span><span class="mi">60</span><span class="o">*</span><span class="mi">24</span><span class="p">]</span><span class="n">val_features</span><span class="p">,</span> <span class="n">val_targets</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="o">-</span><span class="mi">60</span><span class="o">*</span><span class="mi">24</span><span class="p">:],</span> <span class="n">targets</span><span class="p">[</span><span class="o">-</span><span class="mi">60</span><span class="o">*</span><span class="mi">24</span><span class="p">:]</span></pre></div></div></div></div></div><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><p>We now have three datasets - train, val, and test. Train and val are used for training, test is used to determine how well our training went.</p></div></div></div><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><h2 id="Time-to-build-the-network">Time to build the network<a class="anchor-link" href="#Time-to-build-the-network">¶</a></h2><p>Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.</p><p>The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called <em>forward propagation</em>.</p><p>We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called <em>backpropagation</em>.</p><blockquote><p><strong>Hint:</strong> You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.</p></blockquote><p>Below, you have these tasks:</p><ol><li>Implement the sigmoid function to use as the activation function. Set <code>self.activation_function</code> in <code>__init__</code> to your sigmoid function.</li><li>Implement the forward pass in the <code>train</code> method.</li><li>Implement the backpropagation algorithm in the <code>train</code> method, including calculating the output error.</li><li>Implement the forward pass in the <code>run</code> method.</li></ol></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [9]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">NeuralNetwork</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_nodes</span><span class="p">,</span> <span class="n">hidden_nodes</span><span class="p">,</span> <span class="n">output_nodes</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">):</span> <span class="c1"># Set number of nodes in input, hidden and output layers.</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_nodes</span> <span class="o">=</span> <span class="n">input_nodes</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_nodes</span> <span class="o">=</span> <span class="n">hidden_nodes</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_nodes</span> <span class="o">=</span> <span class="n">output_nodes</span> <span class="c1"># Initialize weights</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights_input_to_hidden</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_nodes</span><span class="o">**-</span><span class="mf">0.5</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_nodes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_nodes</span><span class="p">))</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights_hidden_to_output</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_nodes</span><span class="o">**-</span><span class="mf">0.5</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_nodes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_nodes</span><span class="p">))</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">learning_rate</span> <span class="c1">#### Set this to your implemented sigmoid function ####</span> <span class="c1"># Activation function is the sigmoid function</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_function</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">))</span> <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs_list</span><span class="p">,</span> <span class="n">targets_list</span><span class="p">):</span> <span class="c1"># Convert inputs list to 2d array - layer 0</span> <span class="n">inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">inputs_list</span><span class="p">,</span> <span class="n">ndmin</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">T</span> <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets_list</span><span class="p">,</span> <span class="n">ndmin</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">T</span> <span class="c1">#### Implement the forward pass here ####</span> <span class="c1">### Forward pass ###</span> <span class="c1"># TODO: Hidden layer - layer 1</span> <span class="n">hidden_inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights_input_to_hidden</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span> <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_function</span><span class="p">(</span><span class="n">hidden_inputs</span><span class="p">)</span> <span class="c1">#layer 1</span> <span class="c1"># TODO: Output layer - layer 2</span> <span class="n">final_inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights_hidden_to_output</span><span class="p">,</span> <span class="n">hidden_outputs</span><span class="p">)</span> <span class="n">final_outputs</span> <span class="o">=</span> <span class="n">final_inputs</span> <span class="c1">#layer 2</span> <span class="c1">#### Implement the backward pass here ####</span> <span class="c1">### Backward pass ###</span> <span class="c1"># TODO: Output error</span> <span class="n">output_errors</span> <span class="o">=</span> <span class="n">targets</span> <span class="o">-</span> <span class="n">final_outputs</span> <span class="c1">#layer 2 error</span> <span class="c1"># TODO: Backpropagated error</span> <span class="n">hidden_errors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights_hidden_to_output</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">output_errors</span><span class="p">)</span> <span class="n">hidden_grad</span> <span class="o">=</span> <span class="n">hidden_errors</span> <span class="o">*</span> <span class="n">hidden_outputs</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">hidden_outputs</span><span class="p">)</span> <span class="c1"># TODO: Update the weights</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights_hidden_to_output</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">output_errors</span><span class="p">,</span> <span class="n">hidden_outputs</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights_input_to_hidden</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">hidden_grad</span><span class="p">,</span> <span class="n">inputs</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs_list</span><span class="p">):</span> <span class="c1"># Run a forward pass through the network</span> <span class="n">inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">inputs_list</span><span class="p">,</span> <span class="n">ndmin</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">T</span> <span class="c1">#### Implement the forward pass here ####</span> <span class="c1"># TODO: Hidden layer</span> <span class="n">hidden_inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights_input_to_hidden</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span> <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_function</span><span class="p">(</span><span class="n">hidden_inputs</span><span class="p">)</span> <span class="c1"># TODO: Output layer</span> <span class="n">final_inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights_hidden_to_output</span><span class="p">,</span> <span class="n">hidden_outputs</span><span class="p">)</span> <span class="n">final_outputs</span> <span class="o">=</span> <span class="n">final_inputs</span> <span class="k">return</span> <span class="n">final_outputs</span></pre></div></div></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [10]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">MSE</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">Y</span><span class="p">):</span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">y</span><span class="o">-</span><span class="n">Y</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span></pre></div></div></div></div></div><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><h2 id="Training-the-network">Training the network<a class="anchor-link" href="#Training-the-network">¶</a></h2><p>Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.</p><p>You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.</p><h3 id="Choose-the-number-of-epochs">Choose the number of epochs<a class="anchor-link" href="#Choose-the-number-of-epochs">¶</a></h3><p>This is the number of times the dataset will pass through the network, each time updating the weights. As the number of epochs increases, the network becomes better and better at predicting the targets in the training set. You'll need to choose enough epochs to train the network well but not too many or you'll be overfitting.</p><h3 id="Choose-the-learning-rate">Choose the learning rate<a class="anchor-link" href="#Choose-the-learning-rate">¶</a></h3><p>This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.</p><h3 id="Choose-the-number-of-hidden-nodes">Choose the number of hidden nodes<a class="anchor-link" href="#Choose-the-number-of-hidden-nodes">¶</a></h3><p>The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.</p></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [88]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span><span class="c1">### Set the hyperparameters here ###</span><span class="n">epochs</span> <span class="o">=</span> <span class="mi">200</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="n">hidden_nodes</span> <span class="o">=</span> <span class="mi">50</span><span class="n">output_nodes</span> <span class="o">=</span> <span class="mi">1</span><span class="n">N_i</span> <span class="o">=</span> <span class="n">train_features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="n">network</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="n">N_i</span><span class="p">,</span> <span class="n">hidden_nodes</span><span class="p">,</span> <span class="n">output_nodes</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">)</span><span class="n">losses</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'train'</span><span class="p">:[],</span> <span class="s1">'validation'</span><span class="p">:[]}</span><span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span> <span class="c1"># Go through a random batch of 128 records from the training data set</span> <span class="n">batch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">train_features</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span> <span class="k">for</span> <span class="n">record</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">train_features</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">batch</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">train_targets</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">batch</span><span class="p">][</span><span class="s1">'cnt'</span><span class="p">]):</span> <span class="n">network</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">record</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="c1"># Printing out the training progress</span> <span class="n">train_loss</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">train_features</span><span class="p">),</span> <span class="n">train_targets</span><span class="p">[</span><span class="s1">'cnt'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="n">val_loss</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">val_features</span><span class="p">),</span> <span class="n">val_targets</span><span class="p">[</span><span class="s1">'cnt'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"</span><span class="se">\r</span><span class="s2">Progress: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">e</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="n">epochs</span><span class="p">))[:</span><span class="mi">4</span><span class="p">]</span> \ <span class="o">+</span> <span class="s2">"% ... Training loss: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">train_loss</span><span class="p">)[:</span><span class="mi">5</span><span class="p">]</span> \ <span class="o">+</span> <span class="s2">" ... Validation loss: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">val_loss</span><span class="p">)[:</span><span class="mi">5</span><span class="p">])</span> <span class="n">losses</span><span class="p">[</span><span class="s1">'train'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">train_loss</span><span class="p">)</span> <span class="n">losses</span><span class="p">[</span><span class="s1">'validation'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val_loss</span><span class="p">)</span></pre></div></div></div></div><div class="output_wrapper"><div class="output"><div class="output_area"><div class="prompt"></div><div class="output_subarea output_stream output_stdout output_text"><pre>Progress: 99.5% ... Training loss: 0.120 ... Validation loss: 0.257</pre></div></div></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [89]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">losses</span><span class="p">[</span><span class="s1">'train'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Training loss'</span><span class="p">)</span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">losses</span><span class="p">[</span><span class="s1">'validation'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Validation loss'</span><span class="p">)</span><span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span><span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">ymax</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span></pre></div></div></div></div><div class="output_wrapper"><div class="output"><div class="output_area"><div class="prompt output_prompt">Out[89]:</div><div class="output_text output_subarea output_execute_result"><pre>(0.014024930919786877, 1.0)</pre></div></div><div class="output_area"><div class="prompt"></div><div class="output_png output_subarea "><img 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class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><h2 id="Check-out-your-predictions">Check out your predictions<a class="anchor-link" href="#Check-out-your-predictions">¶</a></h2><p>Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.</p></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [90]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span> <span class="o">=</span> <span class="n">scaled_features</span><span class="p">[</span><span class="s1">'cnt'</span><span class="p">]</span><span class="n">predictions</span> <span class="o">=</span> <span class="n">network</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">test_features</span><span class="p">)</span><span class="o">*</span><span class="n">std</span> <span class="o">+</span> <span class="n">mean</span><span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">predictions</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Prediction'</span><span class="p">)</span><span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">((</span><span class="n">test_targets</span><span class="p">[</span><span class="s1">'cnt'</span><span class="p">]</span><span class="o">*</span><span class="n">std</span> <span class="o">+</span> <span class="n">mean</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Data'</span><span class="p">)</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">right</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">predictions</span><span class="p">))</span><span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span><span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">rides</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">test_data</span><span class="o">.</span><span class="n">index</span><span class="p">][</span><span class="s1">'dteday'</span><span class="p">])</span><span class="n">dates</span> <span class="o">=</span> <span class="n">dates</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">d</span><span class="p">:</span> <span class="n">d</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s1">'%b </span><span class="si">%d</span><span class="s1">'</span><span class="p">))</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dates</span><span class="p">))[</span><span class="mi">12</span><span class="p">::</span><span class="mi">24</span><span class="p">])</span><span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">dates</span><span class="p">[</span><span class="mi">12</span><span class="p">::</span><span class="mi">24</span><span class="p">],</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span></pre></div></div></div></div><div class="output_wrapper"><div class="output"><div class="output_area"><div class="prompt"></div><div class="output_png output_subarea "><img 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class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><h2 id="Thinking-about-your-results">Thinking about your results<a class="anchor-link" href="#Thinking-about-your-results">¶</a></h2><p>Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?</p><blockquote><p><strong>Note:</strong> You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter</p></blockquote><h4 id="Your-answer-below">Your answer below<a class="anchor-link" href="#Your-answer-below">¶</a></h4><p>The model does a reasonable job of predicting the data from Dec 11-21. I have expermented with a variety of hyperparameters and it's possible to find a variety of good fits for those dates. However, the predictions never do well for the dates of Dec 22-31 - it always overestimates rentals. I suspect that this is because Christmas week is a dramatic outlier from the rest of the year and might be more pattern than a single hidden layer can match.</p></div></div></div><div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"></div><div class="inner_cell"><div class="text_cell_render border-box-sizing rendered_html"><h2 id="Unit-tests">Unit tests<a class="anchor-link" href="#Unit-tests">¶</a></h2><p>Run these unit tests to check the correctness of your network implementation. These tests must all be successful to pass the project.</p></div></div></div><div class="cell border-box-sizing code_cell rendered"><div class="input"><div class="prompt input_prompt">In [68]:</div><div class="inner_cell"> <div class="input_area"><div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">unittest</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]</span><span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">]</span><span class="n">test_w_i_h</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.3</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]])</span><span class="n">test_w_h_o</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1</span><span class="p">]])</span><span class="k">class</span> <span class="nc">TestMethods</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span> <span class="c1">##########</span> <span class="c1"># Unit tests for data loading</span> <span class="c1">##########</span> <span class="k">def</span> <span class="nf">test_data_path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="c1"># Test that file path to dataset has been unaltered</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="n">data_path</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">'bike-sharing-dataset/hour.csv'</span><span class="p">)</span> <span class="k">def</span> <span class="nf">test_data_loaded</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="c1"># Test that data frame loaded</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">rides</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">))</span> <span class="c1">##########</span> <span class="c1"># Unit tests for network functionality</span> <span class="c1">##########</span> <span class="k">def</span> <span class="nf">test_activation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="n">network</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> <span class="c1"># Test that the activation function is a sigmoid</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">activation_function</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">))))</span> <span class="k">def</span> <span class="nf">test_train</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="c1"># Test that weights are updated correctly on training</span> <span class="n">network</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> <span class="n">network</span><span class="o">.</span><span class="n">weights_input_to_hidden</span> <span class="o">=</span> <span class="n">test_w_i_h</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="n">network</span><span class="o">.</span><span class="n">weights_hidden_to_output</span> <span class="o">=</span> <span class="n">test_w_h_o</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="n">network</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">weights_hidden_to_output</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span> <span class="mf">0.37275328</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.03172939</span><span class="p">]])))</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">weights_input_to_hidden</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span> <span class="mf">0.10562014</span><span class="p">,</span> <span class="mf">0.39775194</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.29887597</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.20185996</span><span class="p">,</span> <span class="mf">0.50074398</span><span class="p">,</span> <span class="mf">0.19962801</span><span class="p">]])))</span> <span class="k">def</span> <span class="nf">test_run</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="c1"># Test correctness of run method</span> <span class="n">network</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> <span class="n">network</span><span class="o">.</span><span class="n">weights_input_to_hidden</span> <span class="o">=</span> <span class="n">test_w_i_h</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="n">network</span><span class="o">.</span><span class="n">weights_hidden_to_output</span> <span class="o">=</span> <span class="n">test_w_h_o</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">network</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">inputs</span><span class="p">),</span> <span class="mf">0.09998924</span><span class="p">))</span><span class="n">suite</span> <span class="o">=</span> <span class="n">unittest</span><span class="o">.</span><span class="n">TestLoader</span><span class="p">()</span><span class="o">.</span><span class="n">loadTestsFromModule</span><span class="p">(</span><span class="n">TestMethods</span><span class="p">())</span><span class="n">unittest</span><span class="o">.</span><span class="n">TextTestRunner</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">suite</span><span class="p">)</span></pre></div></div></div></div><div class="output_wrapper"><div class="output"><div class="output_area"><div class="prompt"></div><div class="output_subarea output_stream output_stderr output_text"><pre>.....----------------------------------------------------------------------Ran 5 tests in 0.003sOK</pre></div></div><div class="output_area"><div class="prompt output_prompt">Out[68]:</div><div class="output_text output_subarea output_execute_result"><pre><unittest.runner.TextTestResult run=5 errors=0 failures=0></pre></div></div></div></div></div> </div> </div></body></html>Great job implementing your network !
Please address issues in my comments and take a bit more time to play with the hyperparameters.
You are right in your observation. The model is a bit overfitted to correctly classify the holiday period. One reasonable answer is the model fails to generalize the Christmas period (aka Holiday period) because it has seen this period only once before (Dec 2011).
All the code in the notebook runs in Python 3 without failing.
Runs smoothly! Well done! 🙌
The sigmoid activation function is implemented correctly
It was perfect to use a lambda function.
Further, to understand the drawbacks of sigmoid and the popular choice ReLU, refer to this link. Understanding the drawbacks of sigmoid and why it's not used anymore will help you develop better models.
All unit tests must be passing
Good job passing all the unit tests!
This is good practice for Test Driven Development, where you write your tests out before you write the code, to make sure that your code behaves as you intend once you've written it!
The input to the hidden layer is implemented correctly in both the train and run methods.
The output of the hidden layer is implemented correctly in both the train and run methods.
The input to the output layer is implemented correctly in both the train and run methods.
The output of the network is implemented correctly in both the train and run methods.
Well done on remembering not to apply a non-linearity (sigmoid function, in this case) to the output layer.
The network output error is implemented correctly
The error propagated back to the hidden layer is implemented correctly
Updates to both the weights are implemented correctly.
Great job implementing weights correctly !
Since now hidden_grad is different than before, you would need to go and change weight_input_to_hidden accordingly.
Hidden layer gradient(hidden_grad) is calculated correctly.
Good job overall with the backward pass! This is a difficult part that most students struggle with, even in graduate school!
Because this is a rather complicated operation (mathematically) it is crucial to make our code for it as clear as possible!
Here are a few changes that will make your correct code much more readable:
# hidden layer gradients
hidden_grad = hidden_errors * hidden_outputs * (1 - hidden_outputs)
Notice that in the first line the comment says you are computing the general hidden gradient function, but in reality you are computing the specific amount of change (error * gradient) that each weight needs to be changed.
While this serves the same practical purpose, and is absolutely correct, it is somewhat misleading to the reader at times, because it might appear that your gradient function is taking other things into account. Remember that the gradient of the sigmoid function is simply S(x) * (1-S(x)).
The number of epochs is chosen such the network is trained well enough to accurately make predictions but is not overfitting to the training data.
You model is starting just starting to converge, 200 is a bit on the low side.
Pro Tip: To decide on a good number of epochs, check out the plot of training and validation loss. After you reach a good loss, the loss on the validation set will start increasing and the error on the training set should keep decreasing as the network is starting to overfit the training data. At this stage it's best to stop iterating.
The number of hidden units is chosen such that the network is able to accurately predict the number of bike riders, is able to generalize, and is not overfitting.
50 is a good choice for hidden_nodes.
Although there is no mathematical explanation for how many hidden units should be used, there have been many empirical studies which suggest that a good rule of thumb is to try an average of the number of input and output nodes. So, for this problem, it should be around 25 (as average is around 28). However, this should not be taken as a hard and fast rule and should only be used as a guideline to design the architecture.
The learning rate is chosen such that the network successfully converges, but is still time efficient.
The chosen learning rate, 0.1 is too high for the model to converge well. When you would be reaching the minima of loss, because of high learning rate, you would probably overshoot and would not be able to achieve minima.
A good way to try optimizing the learning rate for this problem is to start at 0.1 and then keep halving the learning rate and see how the network trains for a fixed number of epochs. You need not run this for a large number of epochs. The idea is to experiment with the learning rate to see which converges faster. Once a range is identified, you can further fine tune it.
Another approach generally employed is gradual decay of learning rate after each (another hyperparameter) epoch. This way, when weights are close to the minima, we take a small step in direction of minima and not overshoot it.
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